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Traditional Japanese medicines, i.e., Kampo medicines, consist of crude drugs (mostly plants) that have empirical pharmacological functions (‘Yakuno’ in Japanese), such as clearing heat. Crude drugs with cold properties, such as Phellodendron bark, have the empirical function of clearing heat as they cool the body. Because we found that anti-inflammatory compounds were present in several crude drugs for clearing heat, it is speculated that the empirical function of clearing heat may be linked to anti-inflammatory activities. When 10 typical crude drugs were selected from 22 herbal crude drugs for clearing heat, we identified anti-inflammatory compounds in five crude drugs, including Phellodendron bark. In this study, the other crude drugs were extracted and partitioned with ethyl acetate (EtOAc) and n-butanol to obtain three crude fractions. All the EtOAc-soluble fractions, except that from Forsythia fruits, inhibited interleukin (IL)-1β-induced nitric oxide (NO) production in primary-cultured rat hepatocytes. Anti-inflammatory compounds were identified from these EtOAc-soluble fractions: baicalein from Scutellaria roots, (−)-nyasol from Anemarrhena rhizomes, and loniflavone from Lonicera leaves and stems. (+)-Phillygenin was purified from Forsythia fruits by removing cytotoxic oleanolic and betulinic acids. These compounds suppressed the production of NO and cytokines in hepatocytes. Anti-inflammatory compounds were not purified from the EtOAc-soluble fraction of Rehmannia roots because of their low abundance. Collectively, these findings indicate that anti-inflammatory compounds are present in all 10 crude drugs for clearing heat, confirming that these anti-inflammatory compounds in crude drugs provide the empirical functions for clearing heat. Other empirical functions of Kampo medicine can also be explained by modern pharmacological activities. Full article

Pre-trained neural networks like GPT-4 and Llama2 have revolutionized intelligent information processing, but their deployment in industrial applications faces challenges, particularly in harsh environments. To address these related issues, model offloading, which involves distributing the computational load of pre-trained models across edge devices, has emerged as a promising solution. While this approach enables the utilization of more powerful models, it faces significant challenges in harsh environments, where reliability, connectivity, and resilience are critical. This paper introduces failure-resilient inference in mobile networks (FRIM), a framework that ensures robust offloading and inference without the need for model retraining or reconstruction. FRIM leverages graph theory to optimize partition redundancy and incorporates an adaptive failure detection mechanism for mobile inference with efficient fault tolerance. Experimental results on DNN models (AlexNet, ResNet, VGG-16) show that FRIM improves inference performance and resilience, enabling more reliable mobile applications in harsh operating environments. Full article

Agricultural navigation, as an essential part of smart agriculture, is a crucial step in realizing intelligence and, compared with the structured features of urban roads, such as lane-keeping lines, traffic guidance lines, etc., the field environment is more complex. Especially at agricultural intersections, traditional navigation line extraction algorithms make it difficult to achieve the automatic prediction of multiple road navigation lines due to complex unstructured features such as weeds and trees. Therefore, this study proposed a field road navigation line prediction method based on an improved context guided network (CGNet), which can quickly, stably, and accurately detect intersection fields and promptly predict navigation lines for two different directional paths at intersections. Firstly, CGNet will be used to learn the local features of intersections and the joint features of video frames before and after the surrounding environment. Then, the CGNet with a self-attention block module is proposed by adding the self-attention mechanism to improve the semantic segmentation accuracy of CGNet in field road scenes, and the detection speed is not significantly reduced. The semantic segmentation accuracy mIoU is 0.89, and the processing speed is 104 FPS. Subsequently, a field road centerline extraction algorithm is proposed based on the partitioning idea, which can accurately obtain the centerlines of road intersections in the image. The average lateral deviation of each centerline is less than 4%. This study achieved the prediction of intersection navigation lines in mountainous field road scenes, which can provide technical support for field operation road planning of agricultural equipment such as plant protection and harvesting. At the same time, the research findings provide theoretical references for sustainable agricultural development. Full article

Deep learning (DL) technology has shown outstanding performance in various fields such as object recognition and classification, speech recognition, and natural language processing. However, it is well known that DL models are vulnerable to data poisoning attacks, where adversaries modify or inject data samples maliciously during the training phase, leading to degraded classification accuracy or misclassification. Since data poisoning attacks keep evolving to avoid existing defense methods, security researchers thoroughly examine data poisoning attack models and devise more reliable and effective detection methods accordingly. In particular, data poisoning attacks can be realistic in an adversarial situation where we retrain a DL model with a new dataset obtained from an external source during transfer learning. By this motivation, we propose a novel defense method that partitions and inspects the new dataset and then removes malicious sub-datasets. Specifically, our proposed method first divides a new dataset into n sub-datasets either evenly or randomly, inspects them by using the clean DL model as a poisoned dataset detector, and finally removes malicious sub-datasets classified by the detector. For partition and inspection, we design two dynamic defensive algorithms: the Sequential Partitioning and Inspection Algorithm (SPIA) and the Randomized Partitioning and Inspection Algorithm (RPIA). With this approach, a resulting cleaned dataset can be used reliably for retraining a DL model. In addition, we conducted two experiments in the Python and DL environment to show that our proposed methods effectively defend against two data poisoning attack models (concentrated poisoning attacks and random poisoning attacks) in terms of various evaluation metrics such as removed poison rate (RPR), attack success rate (ASR), and classification accuracy (ACC). Specifically, the SPIA completely removed all poisoned data under concentrated poisoning attacks in both Python and DL environments. In addition, the RPIA removed up to 91.1% and 99.1% of poisoned data under random poisoning attacks in Python and DL environments, respectively. Full article

Using hybrid energy storage systems, we propose a power mode partitioning control strategy for AC/DC microgrids that effectively mitigates frequency and voltage fluctuations. By incorporating composite virtual impedance, we categorize the state of charge (SOC) into five distinct operational modes. Supercapacitors, known for their dynamic response, are prioritized to counteract power fluctuations. We assign these operational modes to the AC and DC subnetworks based on real-time changes in frequency and voltage. When significant fluctuations occur, coordinated power transmission between the subnetworks and support from the energy storage system ensure that frequency and voltage remain within acceptable limits. Simulations conducted in MATLAB/Simulink confirmed that this control strategy stabilized power fluctuations and addressed the challenges of overcharging and overdischarging in storage batteries. Full article

Manufacturers increasingly turn to robotic gripper designs to improve the efficiency of gripping and moving objects and provide greater flexibility to these objects. Neuro-fuzzy techniques are the most widespread in developing gripper designs. In this study, the traditional gripper design is modified by adding a suitable cam that makes it compatible with the basic design, and an adaptive neuro-fuzzy inference system (ANFIS) is used in a MATLAB Simulink environment. The developed gripper investigates the follower path concerning the cam surface curve, and the gripper position is controlled using the developed ANFIS-PID. Three methods are examined in the developed ANFIS-PID controller: grid partitioning (genfis1), subtractive clustering (genfis2), and fuzzy C-means clustering (genfis3). The results show that the added cam can improve the gripping strength and that the ANFIS-PID model effectively handles the rise time and supported settling time. The developed ANFIS-PID controller demonstrates more efficient performance than Fuzzy-PID and traditional tuned-PID controllers. This proposed controller does not achieve any overshoot, and the rise time is improved by approximately 50–51%, and the steady-state error is improved by 75–95%, compared with Fuzzy-PID and tuned PID controllers. Moreover, the developed ANFIS-PID controller provides more stability for a wide range of set point displacements—0.05 cm, 0.5 cm, and 1.5 cm—during the testing period. The developed ANFIS-PID controller is not affected by disturbance, making it well suited for robotic gripper designs. Grip force control is also investigated using the proposed ANFIS-PID controller and compared with the Fuzzy-PID in three scenarios. The result from this force control proves objects’ higher actual gripping performance by using the proposed ANFIS-PID. Full article

High-speed rail (HSR) environments present unique challenges due to their high mobility and dense passenger traffic, resulting in dynamic and unpredictable task generation patterns. Mobile Edge Computing (MEC) has emerged as a transformative paradigm to address these challenges by deploying computation resources closer to end-users. However, the limited resources of MEC servers necessitate efficient task partitioning, wherein a single task is divided into multiple sub-tasks for parallel processing across MEC servers. In the context of HSR environments, the task partitioning ratio is pivotal in ensuring quality of service (QoS) and optimizing resource utilization, particularly under dynamic and high-demand conditions. This paper proposes a deep reinforcement learning (DRL)-based task partitioning mechanism using Twin Delayed Deep Deterministic Policy Gradient (TD3) for HSR environments with MEC servers (MECSs). The proposed method dynamically adjusts task partitioning ratios by leveraging real-time information about task characteristics and server load conditions. The experimental results show that when the task arrival rate is 20, the delay is improved by about 5% compared to random and about 13% compared to no_partition. When it is 50, there is no significant difference from random and about 2% improvement compared to no_partition. The task throughput is almost the same when it is 20. However, when it is 50, random is much better. We also looked at the performance change according to the number of serving MECSs. In this process, we can also note the research direction of finding an appropriate number of serving MECSs K. The results highlight the efficacy of DRL-based approaches in dynamically adapting to the unique characteristics of HSR environments, achieving optimal resource allocation and maintaining high QoS. This paper contributes to advancing task partitioning strategies for HSR systems and lays the groundwork for future research in MEC-based HSR systems. Full article

We consider two specific families of binomial trees and forests: simply generated binomial d-ary trees and forests versus their increasing phylogenetic version, with tree nodes in increasing order from the root to any of its leaves. The analysis (both pre-asymptotic and asymptotic) consists of some of the main statistical features of their total progenies. We take advantage of the fact that the random distribution of those trees are obtained while weighting the counts of the underlying combinatorial trees. We finally briefly stress a rich alternative randomization of combinatorial trees and forests, based on the ratio of favorable count outcomes to the total number of possible ones. Full article

(1) Background: This study is designed to establish thyroid-stimulating hormone (TSH) reference intervals tailored to different neonatal age groups and Indonesian local populations. (2) Methods: Dried blood spot neonatal TSH values, from 1 January 2022 to 31 December 2023, were used to establish the neonatal TSH reference intervals partitioned by sex, gestational age, and ethnic group at different neonatal ages. (3) Results: A significant difference in the reference intervals value was observed in sex, gestational ages, and parental ethnicity groups in different neonatal age subgroups (p < 0.05). Male reference intervals were significantly higher than those of females at all neonatal ages. Late and post-term gestational age categories reference intervals were higher than early and full-term. Among the ethnic groups, Madurese had a higher upper limit TSH reference interval. (4) Conclusions: Our neonatal TSH reference intervals were needed to provide a reference adapted to the local population of Indonesia. Full article

The present study employed a ⁶⁵Zn radioactive isotope as a tracer to investigate the foliar uptake and distribution patterns of ZnO concentrated suspension- and ZnSO₄ solution-sprayed on soybean plant leaves. The radiolabeled foliar treatments were sprayed on the leaves at V4 and V8 phenological stages. The radioactivity of ⁶⁵Zn in the leaves, roots, stems, and pods was determined using γ-ray spectrometry. After the first foliar spray, V4, the partition of radiolabeled Zn in plants treated with ZnO and ZnSO₄ was 99.22% and 98.12% in treated leaves, 0.15% and 0.39% in stems, 0.16% and 0.29% in roots, and 0.47% and 1.19% in newly expanded non-treated leaves, respectively. After two sprayings, V4 and V8, the partition of radiolabeled Zn in plants treated with ZnO and ZnSO₄ was 92.56% and 92.18% in treated leaves, 0.92% and 0.70% in stems, 0.52% and 0.39% in roots, 5.60% and 6.15% in newly expanded non-treated leaves, and 0.43% and 0.61% in grains, respectively. The total fraction translocated from the application tissue was 0.79% and 1.91% for ZnO and ZnSO₄, respectively, after 12 days and 8.03% and 8.48% for ZnO and ZnSO₄, respectively, after 72 days. An anatomical analysis revealed that plants cultivated in a nutrition solution with 10% ionic strength had 63% fewer stomata, and the xylem vessels were 63% smaller compared to plants grown in a solution with 100% Zn ionic. One can conclude that after a short period, 12 days, the absorption and translocation of ZnSO₄ was higher and faster than ZnO, and after the long period, 72 days, their performance was similar. Full article

Research on the calculation method of deep-buried surrounding rock pressure is an important subject in engineering mathematics. The existing calculation methods are mainly the two-hole closely spaced tunnel, double-arch tunnel, three-hole closely spaced tunnel, and three-arch tunnel. The four-hole closely spaced double-arch tunnel has the characteristics of both the double-arch tunnel and closely spaced tunnel, and its surrounding rock pressure distribution is more complicated. In this paper, the research is carried out based on Protodyakonov’s theory and the concept of process load. The influence of the post-construction tunnel on the supporting structure of the pre-construction tunnel is also considered. The calculation model of the surrounding rock pressure of the deep-buried four-hole closely spaced double-arch tunnel is established, and the calculation formula of the surrounding rock pressure is deduced and verified. Finally, the influences of the rock column parameters, excavation procedure, tunnel span, and middle partition wall on the surrounding rock pressure are analyzed. Full article

As animal welfare becomes more active in livestock industry, research is being conducted on ways to improve poor housing environments, reduce stress, and meet welfare standards. Among these, environmental enrichment methods are effective in reducing stress and creating a welfare-friendly rearing environment, but there are few cases of actual application to farms. Therefore, we aimed to investigate the effect of providing pecking materials (grain blocks), known as one of the environmental enrichment methods, to commercial broiler farms. This study used two facilities that could accommodate 32,000 one-day-old broilers (Arbor acres) per building, and two groups (control and treatment groups) were designed after creating two identical areas within each building (total two treatments, two replicates, 16,000 birds per replicate). Two identical zones within the house were created by installing a partition in the center; one side was provided with grain blocks (one grain block per 1000 birds), and the other side was not. Analysis items included productivity (body weight, uniformity), environmental variables (litter and air), welfare indicators (leg, gait score, feather cleanliness score), and serum corticosterone levels. Analysis of all items was conducted twice, on the 19th and 27th, taking into account the farm’s feed change date and slaughter schedule. Other environmental conditions (density, lighting, ventilation, temperature, humidity, feed, and water) were the same. As a result, no difference in productivity was observed according to enrichment, and the quality of litter and air was similar. Also, there was no significant difference in welfare indicators. Interestingly, however, provision of the environment enrichment lowered serum corticosterone levels (p < 0.05). The implications of our study are that grain blocks as a pecking material are an effective way to reduce stress without adversely affecting broiler performance and rearing environment. However, it is still necessary to explore optimal enrichment materials that can help not only the welfare level but also the broiler performance. Full article

The interaction between forests and precipitation plays a crucial role in the material cycling of forest ecosystems. Atmospheric deposition and rainfall leaching promote the transfer of heavy metals to the forest floor, while canopy exchange may potentially slow this process. Therefore, studying heavy metal fluxes and their influencing factors, along with canopy rainfall partitioning, is essential for understanding forest material cycling. We conducted a year-long experiment to examine the dynamics of chromium (Cr), cadmium (Cd), and lead (Pb) concentrations and fluxes in four types of forests (Cunninghamia lanceolata plantations, Castanopsis carlesii plantations, Cas. carlesii natural forests, and Cas. carlesii secondary forests) located in the subtropical regions of southeast China. Results showed that (1) the annual mean concentrations of Cr, Cd, and Pb were 167.6, 13.8, and 6180.5 μg L⁻¹ in the throughfall and 204.7, 28.4, and 2251.1 μg L⁻¹ in the stemflow, respectively, and the annual fluxes of Cr, Cd, and Pb through throughfall were 29.3, 2.4, and 847.7 g ha⁻¹, respectively, and were 1.7, 0.2, and 12.7 g ha⁻¹ through stemflow, respectively; (2) the concentrations of these heavy metals associated with throughfall did not vary between forest types, but their fluxes were highest in Cas. carlesii natural forests; (3) Cr concentration and flux were higher during the rainy than dry seasons, while Cd and Pb concentrations and fluxes showed an opposite trend. Overall, our results indicate that the fluxes of heavy metals along with rainfall partitioning were highest in natural forests and are primarily controlled by meteorological factors, indicating that the conversion of natural forests to other forest types will substantially change the fluxes of heavy metals along with hydrological processes. These results will contribute to a better understanding of the natural fluxes of heavy metals in forest ecosystems and are valuable for sustainable forest management, particularly in the context of forest type transformation. Full article

Objectives: Semen analysis is universally regarded as the gold standard for diagnosing male infertility, while ultrasonography plays a vital role as a complementary diagnostic tool. This study aims to assess the effectiveness of artificial intelligence (AI)-driven deep learning algorithms in predicting semen analysis parameters based on testicular ultrasonography images. Materials and Methods: This study included male patients aged 18–54 who sought evaluation for infertility at the Urology Outpatient Clinic of our hospital between February 2022 and April 2023. All patients underwent comprehensive assessments, including blood hormone profiling, semen analysis, and scrotal ultrasonography, with each procedure being performed by the same operator. Longitudinal-axis images of both testes were obtained and subsequently segmented. Based on the semen analysis results, the patients were categorized into groups according to sperm concentration, progressive motility, and morphology. Following the initial classification, each semen parameter was further subdivided into “low” and “normal” categories. The testicular images from both the right and left sides of all patients were organized into corresponding folders based on their associated laboratory parameters. Three distinct datasets were created from the segmented images, which were then augmented. The datasets were randomly partitioned into an 80% training set and a 20% test set. Finally, the images were classified using the VGG-16 deep learning architecture. Results: The area under the curve (AUC) values for the classification of sperm concentration (oligospermia versus normal), progressive motility (asthenozoospermia versus normal), and morphology (teratozoospermia versus normal) were 0.76, 0.89, and 0.86, respectively. Conclusions: In our study, we successfully predicted semen analysis parameters using data derived from testicular ultrasonography images through deep learning algorithms, representing an innovative application of artificial intelligence. Given the limited published research in this area, our study makes a significant contribution to the field and provides a foundation for future validation studies. Full article

Background/Objectives: Following tooth extraction, resveratrol (RSV) can support healing by reducing inflammation and microbial risks, though its poor solubility limits its effectiveness. This study aims to develop a solid nanocomposite by embedding RSV in lipid nanoparticles (mLNP) within a hydrophilic matrix, to the scope of improving local delivery and enhancing healing. Hydroxyapatite (HXA), often used as a bone substitute, was added to prevent post-extraction alveolus volume reduction. Methods: The mLNP-RSV dispersion was mixed with seven different polymers in various mLNP/polymer ratios. Following freeze-drying, the powders were redispersed, and the resulting dispersions were tested by DLS experiments. Then, the best two nanocomposites underwent extensive characterization by SEM, XRD, FTIR, Raman spectroscopy, and thermal analysis as well as in vitro partitioning studies aimed at verifying their ability to yield the mLNP-RSV from the hydrophilic matrix to a lipophilic tissue. The characterizations led to identify the best nanocomposite, which was further combined with HXA to obtain hybrid nanocomposites, further evaluated as pharmaceutical powders or in form of mini-tablets. Results: PEG-based nanocomposites emerged as optimal and, following HXA insertion, the resulting powders revealed adequate bulk properties, making them useful as a pharmaceutical intermediate to produce ≈59 mm³ mini-tablets, compliant with the post-extraction socket. Moreover, they were proven ex vivo to be able to promote RSV and GA accumulation into the buccal tissue over time. Conclusions: The here-proposed mini-tablet offers an innovative therapeutic approach for alveolar wound healing promotion as they led to a standardized dose administration, while being handy and stable in terms of physical solid identity as long as it takes to suture the wound. Full article